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Saturday, 28 November 2020

Neural Network | Activation Function | Cost Function | Python | Tensorflow | 2020

IDEA of Neural Network :

We have already seen that how a single perceptron behaves, how let's explore this concept to the idea of a neural networks.

Now, let's how to connect many neurons (Perceptrons) together and then how to represent it mathematically.

Multiple Rerceptrons Network :- 

There are three layer -

1 INPUT LAYER :- 

  • Actual values of dataset

2. HIDDEN LAYERS :-

  • These are layers in between inputs and outputs.
  • If you have 3 or more layers then it will be Deep Neural Network.

3. Output Layer :-

  • It contains the final output.

As you go forward through more layers, the level of abstraction increases.

Now let's discuss the activation function in a little more detail.


ACTIVATION FUNCTION:-

Previously our activation function was just a simple function that output 0 and 1.

It would be nice if we could have a more dynamic function for example.

Rectified Linear Unit (ReLU) - This is the most useful and relatively simple function, max(0,z).

The Gradient Descent :-

Gradient descent is an optimization algorithm for finding the minimum of a function.

Gradient descent (in 1 dimension)